Posts Tagged ‘games’

Run-Time Behavior Adaptation for Real-Time Interactive Games

Intelligent agents working in real-time domains need to adapt to changing circumstance so that they can improve their performance and avoid their mistakes. AI agents designed for interactive games, however, typically lack this ability. Game agents are traditionally implemented using static, hand-authored behaviors or scripts that are brittle to changing world dynamics and cause a break in player experience when they repeatedly fail. Furthermore, their static nature causes a lot of effort for the game designers as they have to think of all imaginable circumstances that can be encountered by the agent. The problem is exacerbated as state-of-the-art computer games have huge decision spaces, interactive user input, and real-time performance that make the problem of creating AI approaches for these domains harder.

In this paper we address the issue of non-adaptivity of game playing agents in complex real-time domains. The agents carry out run-time adaptation of their behavior sets by monitoring and reasoning about their behavior execution to dynamically carry out revisions on the behaviors. The behavior adaptation approaches has been instantiated in two real-time interactive game domains. The evaluation results shows that the agents in the two domains successfully adapt themselves by revising their behavior sets appropriately.

Read the paper:

Run-Time Behavior Adaptation for Real-Time Interactive Games

by Manish Mehta, Ashwin Ram

IEEE Transactions on Computational Intelligence and AI in Games, Vol. 1, No. 3, September 2009

Using Meta-Reasoning to Improve the Performance of Case-Based Planning

Case-based planning (CBP) systems are based on the idea of reusing past successful plans for solving new problems. Previous research has shown the ability of meta-reasoning approaches to improve the performance of CBP systems. In this paper we present a new meta-reasoning approach for autonomously improving the performance of CBP systems that operate in real-time domains.

Our approach uses failure patterns to detect anomalous behaviors, and it can learn from experience which of the failures detected are important enough to be fixed. Finally, our meta-reasoning approach can exploit both successful and failed executions for meta-reasoning.

We illustrate its benefits with experimental results from a system implementing our approach called Meta-Darmok in a real-time strategy game. The evaluation of Meta-Darmok shows that the system successfully adapts itself and its performance improves through appropriate revision of the case base.

Read the paper:

Using Meta-Reasoning to Improve the Performance of Case-Based Planning

by Manish Mehta, Santi Ontañón, Ashwin Ram

International Conference on Case-Based Reasoning (ICCBR-09), Seattle, July 2009

Authoring Behaviors for Games using Learning from Demonstration

Behavior authoring for computer games involves writing behaviors in a programming language. This method is cumbersome and requires a lot of programming effort to author the behavior sets. Further, this approach restricts the behavior set authoring to people who are experts in programming.

This paper describes our approach to design a system that allows a user to demonstrate behaviors to the system, which the system uses to learn behavior sets for a game domain. With learning from demonstration, we aim at removing the requirement that the user has to be an expert in programming, and only require him to be an expert in the game. The approach has been integrated in a easy-to-use visual interface and instantiated for two domains, a real-time strategy game and an interactive drama.

Read the paper:

Authoring Behaviors for Games using Learning from Demonstration

by Manish Mehta, Santiago Ontañón, Tom Amundsen, Ashwin Ram

ICCBR-09 Workshop on Case-Based Reasoning for Computer Games, Seattle, July 2009

Learning from Human Demonstrations for Real-Time Case-Based Planning

One of the main bottlenecks in deploying case-based planning systems is authoring the case-base of plans. In this paper we present a collection of algorithms that can be used to automatically learn plans from human demonstrations. Our algorithms are based on the basic idea of a plan dependency graph, which is a graph that captures the dependencies among actions in a plan. Such algorithms are implemented in a system called Darmok 2 (D2), a case-based planning system capable of general game playing with a focus on real-time strategy (RTS) games. We evaluate D2 with a collection of three different games with promising results.

Read the paper:

Learning from Human Demonstrations for Real-Time Case-Based Planning

by Santi Ontañón, Kane Bonnette, Praful Mahindrakar, Marco Gómez-Martin, Katie Long, Jai Radhakrishnan, Rushabh Shah, Ashwin Ram

IJCAI-09 Workshop on Learning Structural Knowledge from Observations, Pasadena, CA, July 2009

Using First Order Inductive Learning as an Alternative to a Simulator in a Game Artificial Intelligence

Currently many game artificial intelligences attempt to determine their next moves by using a simulator to predict the effect of actions in the world. However, writing such a simulator is time-consuming, and the simulator must be changed substantially whenever a detail in the game design is modified. As such, this research project set out to determine if a version of the first order inductive learning algorithm could be used to learn rules that could then be used in place of a simulator.

We used an existing game artificial intelligence system called Darmok 2. By eliminating the need to write a simulator for each game by hand, the entire Darmok 2 project could more easily adapt to additional real-time strategy games. Over time, Darmok 2 would also be able to provide better competition for human players by training the artificial intelligences to play against the style of a specific player. Most importantly, Darmok 2 might also be able to create a general solution for creating game artificial intelligences, which could save game development companies a substantial amount of money, time, and effort.

Read the thesis:

Using First Order Inductive Learning as an Alternative to a Simulator in a Game Artificial Intelligence

by Katie Long

Undergraduate Thesis, College of Computing, Georgia Institute of Technology, Atlanta, GA, 2009

Creating Behavior Authoring Environments for Everyday Users

The design of interactive experiences is increasingly important in our society. Examples include interactive media, computer games, and interactive portals. There is increasing interest in modes of interaction with virtual characters, as they represent a natural way for humans to interact. Creating such characters is a complex task, requiring both creative skills (to design personalities, emotions, gestures, behaviors) and programming skills (to code these in a scripting or programming language). There is little understanding of how the behavior authoring process can be simplified with easy-to-use authoring environments that can support the cognitive needs of everyday users and help them at every step to easily carry out this creative task.

Our research focuses on behavior authoring environments that not only make it easy for novices/everyday users to create characters but also provide them scaffolding in designing these interactive experiences. In this paper we present results from a user study with a paper prototype of an authoring environment that is aimed to allow everyday users to create virtual characters. The study aims at determining whether typical computer users are able to create character personalities in specific scenarios and think about characters’ mental states, and if so, then what kinds of user interfaces would be suitable for this authoring environment.

Read the paper:

Creating Behavior Authoring Environments for Everyday Users

by Manish Mehta, Christina Lacey, Iulian Radu, Abhishek Jain, Ashwin Ram

International Conference on Computer Games, Multimedia, and Allied Technologies (CGAT-09), Singapore, May 2009

On-Line Case-Based Planning

Some domains, such as real-time strategy (RTS) games, pose several challenges to traditional planning and machine learning techniques. In this paper, we present a novel on-line case-based planning architecture that addresses some of these problems. Our architecture addresses issues of plan acquisition, on-line plan execution, interleaved planning and execution and on-line plan adaptation. We also introduce the Darmok system, which implements this architecture in order to play Wargus (an open source clone of the well-known RTS game Warcraft II). We present empirical evaluation of the performance of Darmok and show that it successfully learns to play the Wargus game.

Read the paper:

On-Line Case-Based Planning

by Santi Ontañón, Neha Sugandh, Kinshuk Mishra, Ashwin Ram

Computational Intelligence, 26(1):84-119, 2010.

Developing a Drama Management Architecture for Interactive Fiction Games

A growing research community is working towards employing drama management components in interactive story-based games. These components gently guide the story towards a narrative arc that improves the player’s experience. In this paper we present our Drama Management architecture for real-time interactive story games that has been connected to a real graphical interactive story based on the Anchorhead game. We also report on the natural language understanding system that has been incorporated in the system and report on a user study with an implementation of our DM architecture.

Developing a Drama Management Architecture for Interactive Fiction Games

by Santi Ontañón, Abhishek Jain, Manish Mehta, Ashwin Ram

1st Joint International Conference on Interactive Digital Storytelling (ICIDS-08), Erfurt, Germany, November 2008

Stochastic Plan Optimization in Real-Time Strategy Games

We present a domain independent off-line adaptation technique called Stochastic Plan Optimization for finding and improving plans in real-time strategy games. Our method is based on ideas from genetic algorithms, but we utilize a different representation for our plans and an alternate initialization procedure for our search process. The key to our technique is the use of expert plans to initialize our search in the most relevant parts of plan space. Our experiments validate this approach using our existing case based reasoning system Darmok in the real-time strategy game Wargus, a clone of Warcraft II.

Read the paper:

Stochastic Plan Optimization in Real-Time Strategy Games

by Andrew Trusty, Santi Ontañón, Ashwin Ram

4th Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-08), Stanford, CA, October 2008

An Intelligent IDE for Behavior Authoring in Real-Time Strategy Games

Behavior authoring for computer games involves writing behaviors in a programming language and then iteratively refining them by detecting issues with them. The main bottlenecks are a) the effort required to author the behaviors and b) the revision cycle as, for most games, it is practically impossible to write a behavior for the computer game AI in a single attempt. The main problem is that the current development environments (IDE) are typically mere text editors that can only help the author by pointing out syntactical errors.

In this paper we present an intelligent IDE (iIDE) that has the following capabilities: it allows the author to program initial versions of the behaviors through demonstration, presents visualizations of behavior execution for revision, lets the author define failure conditions on the existing behavior set, and select appropriate fixes for the failure conditions to correct the behaviors. We describe the underlying techniques that support these capabilities inside our implemented iIDE and the future steps that need to be carried out to improve the iIDE. We also provide details on a preliminary user study showing how the new features inside the iIDE can help authors in behavior authoring and debugging in a real-time strategy game.

Read the paper:

An Intelligent IDE for Behavior Authoring in Real-Time Strategy Games

by Manish Mehta, Suhas Virmani, Yatin Kanetkar, Santi Ontañón, Ashwin Ram

4th Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-08), Stanford, CA, October 2008